Expert¶

Description¶

Trajectories have expert data from a fine-tuned RL policy provided in the DAPG repository. The environment used to collect the dataset is AdroitHandRelocate-v1.

Dataset Specs¶


Total Steps	1000000
Total Episodes	5000
Dataset Observation Space	`Box(-inf, inf, (39,), float64)`
Dataset Action Space	`Box(-1.0, 1.0, (30,), float32)`
Algorithm	Not provided
Author	Rodrigo de Lazcano
Email	rperezvicente@farama.org
Code Permalink	https://github.com/rodrigodelazcano/d4rl-minari-dataset-generation
Minari Version	`0.4.3` (supported)
Download	`minari download D4RL/relocate/expert-v2`

Environment Specs¶

The following table rows correspond to the Gymnasium environment specifications used to generate the dataset. To read more about what each parameter means you can have a look at the Gymnasium documentation https://gymnasium.farama.org/api/registry/#gymnasium.envs.registration.EnvSpec

This environment can be recovered from the Minari dataset as follows:

import minari

dataset = minari.load_dataset('D4RL/relocate/expert-v2')
env  = dataset.recover_environment()


ID	AdroitHandRelocate-v1
Observation Space	`Box(-inf, inf, (39,), float64)`
Action Space	`Box(-1.0, 1.0, (30,), float32)`
entry_point	`gymnasium_robotics.envs.adroit_hand.adroit_relocate:AdroitHandRelocateEnv`
max_episode_steps	200
reward_threshold	None
nondeterministic	`False`
order_enforce	`True`
disable_env_checker	`False`
kwargs	`{'reward_type': 'dense'}`
additional_wrappers	`()`
vector_entry_point	`None`

Evaluation Environment Specs¶

This dataset doesn’t contain an eval_env_spec attribute which means that the specs of the environment used for evaluation are the same as the specs of the environment used for creating the dataset. The following calls will return the same environment:

import minari

dataset = minari.load_dataset('D4RL/relocate/expert-v2')
env  = dataset.recover_environment()
eval_env = dataset.recover_environment(eval_env=True)

assert env.spec == eval_env.spec